LINselect (version 1.1.5)

tuneLasso: tuneLasso

Description

tune the lasso parameter in the regression model : \(Y= X \beta + \sigma N(0,1)\) using the lasso or the gauss-lasso method

Usage

tuneLasso(Y, X, normalize = TRUE, method = c("lasso", "Glasso"), 
    dmax = NULL, Vfold = TRUE, V = 10, LINselect = TRUE, a = 0.5, 
    K = 1.1, verbose = TRUE, max.steps = NULL)

Value

A list with one or two components according to method.

lasso if method contains "lasso" is a list with one or two components according to Vfold and LINselect.

• Ls if LINselect=TRUE. A list with components

  • support: vector of integers. Estimated support of the parameter vector \(\beta\).

  • coef: vector whose first component is the estimated intercept.
    The other components are the estimated non zero coefficients.

  • fitted: vector with length n. Fitted value of the response.

  • crit: vector containing the values of the criteria for each value of lambda.

  • lambda: vector containing the values of the tuning parameter of the lasso algorithm.

• CV if Vfold=TRUE. A list with components

  • support: vector of integers. Estimated support of the parameter vector \(\beta\).

  • coef: vector whose first component is the estimated intercept.
    The other components are the estimated non zero coefficients.

  • fitted: vector with length n. Fitted value of the response.

  • crit: vector containing the values of the criteria for each value of lambda.

  • crit.err: vector containing the estimated standard-error of the criteria.

  • lambda: vector containing the values of the tuning parameter of the lasso algorithm.

Glasso if method contains "Glasso". The same as lasso.

Arguments

Y

vector with n components : response variable.

X

matrix with n rows and p columns : covariates.

normalize

logical : corresponds to the input normalize of the functions enet and cv.enet.
If TRUE the variates X are normalized.

method

vector of characters whose components are subset of (“lasso”, “Glasso”)

dmax

integer : maximum number of variables in the lasso estimator. dmax \(\le\) D where
D = min (3*p/4 , n-5) if p\( \ge \)n
D= min(p,n-5) if p < n.
Default : dmax = D.

Vfold

logical : if TRUE the tuning is done by Vfold-CV

V

integer. Gives the value of V in the Vfold-CV procedure

LINselect

logical : if TRUE the tuning is done by LINselect

a

scalar : value of the parameter \(\alpha\) in the LINselect criteria

K

scalar : value of the parameter \(K\) in the LINselect criteria

verbose

logical : if TRUE a trace of the current process is displayed in real time.

max.steps

integer : maximum number of steps in the lasso procedure.
Corresponds to the input max.steps of the function enet.
Default : max.steps = 2*min(p,n)

Author

Yannick Baraud, Christophe Giraud, Sylvie Huet

References

See Baraud et al. 2010 http://hal.archives-ouvertes.fr/hal-00502156/fr/
Giraud et al., 2013, https://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.ss/1356098553

Examples

#source("charge.R")
library("LINselect")

# simulate data with
if (FALSE) ex <- simulData(p=100,n=100,r=0.8,rSN=5)

if (FALSE) ex1.tuneLasso <- tuneLasso(ex$Y,ex$X)

if (FALSE) data(diabetes)
if (FALSE) attach(diabetes)
if (FALSE) ex.diab <- tuneLasso(y,x2)
if (FALSE) detach(diabetes)